Both the TRANSREG and PRINQUAL procedures are data transformation procedures that have many of the same transformations. These procedures can either directly perform the specified transformation (such as taking the logarithm of the variable) or search for an optimal transformation (such as a spline with a specified number of knots). Both procedures can use an iterative, alternating least squares analysis. Both procedures create an output data set that can be used as input to other procedures. PROC PRINQUAL displays relatively little output, whereas PROC TRANSREG displays many results. PROC TRANSREG has two sets of variables, usually dependent and independent, and it fits linear models such as ordinary regression and ANOVA, multiple and multivariate regression, metric and nonmetric conjoint analysis, metric and nonmetric vector and ideal point preference mapping, redundancy analysis, canonical correlation, and response surface regression. In contrast, PROC PRINQUAL has one set of variables, fits a principal component model or multidimensional preference analysis, and can also optimize other properties of a correlation or covariance matrix. PROC TRANSREG performs hypothesis testing and can be used to code experimental designs prior to their use in other analyses. PROC TRANSREG can also perform Box-Cox transformations and fit models with smoothing spline and penalized B-spline transformations.
See Chapter 4: Introduction to Regression Procedures, for comparisons of the TRANSREG and REG procedures.